Apparatus and method for enhanced message targeting

ABSTRACT

A method, apparatus, and computer program product are disclosed for improved machine learning using a statistical model. In the context of an apparatus, some example embodiments include a processor configured to cause retrieval of information regarding a plurality of consumers, and modeling circuitry configured to train a statistical model of the plurality of consumers based on the retrieved information, and predict, using the statistical model, an incremental booking value associated with the promotion for each consumer of the plurality of consumers. The processor is further configured to select a subset of the plurality of consumers for receiving impressions of the promotion. Some example embodiments may further include communications circuitry configured to transmit an impression of the promotion to each consumer in the subset of the plurality of consumers.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 62/140,025 and U.S. Provisional Patent Application No.62/140,030, each titled “Apparatus and Method for Enhanced MessageTargeting,” each filed Mar. 30, 2015, and each incorporated by referenceherein in its entirety.

TECHNOLOGICAL FIELD

Example embodiments of the present invention relate generally to machinelearning based on statistical modeling and, more particularly, to anapparatus and method for predictively modeling user behavior fortargeting marketing incentives to users.

BACKGROUND

Applicants have discovered problems with existing mechanisms fortargeting correspondence using programmatic electronic systems. Throughapplied effort, ingenuity, and innovation, Applicants have solved manyof these identified problems by developing a solution that is embodiedby the present invention and described in detail below.

BRIEF SUMMARY

A novel system is provided for targeting marketing messages to users.For example, machine learning techniques may be used to train astatistical model for user behavior prediction and optimization ofmessaging functions. Advantageously, the capabilities of the underlyingmachine learning systems are therefore improved. By selectivelygenerating and sending messages in accordance with the output of thetrained statistical model, improvements to the efficient use ofcomputing resources are achieved in terms of processing, networking, anddata storage. Furthermore, promotion and marketing systems may servenumerous users or consumers, tracking consumer interactions with theseelectronic systems (e.g., over a network such as the Internet). Thelarge volumes of consumer data collected by these systems make manualanalysis inefficient and effectively unworkable. Thus the techniquesdiscussed herein also provide for programmatic “big data” handling ofcollected user data.

Furthermore, example embodiments described herein are designed tomaximize revenue received in response to customer relationship campaignsrun by a promotion and marketing service by carefully choosing consumersto whom different promotions are offered. The procedures for choosingwhich promotions to offer to which consumers are based on anarchitecture that models consumer behavior using a wide variety offactors and predicts an incremental booking value for a particularpromotion and a particular consumer. The factors used to develop themodel may comprise electronic marketing information, which may includeinformation regarding consumer purchase history, consumer activity(e.g., clickstream data), location data, demographic information, or thelike. By modeling consumer behavior and predicting expected incrementalbooking values of a consumer population, example embodiments facilitatethe delivery of promotions to a subset of the consumer population thatmaximize merchants' expected revenues.

In a first example embodiment, an apparatus is provided for improvedmachine learning using a statistical model to facilitate improvedtargeting of a promotion. The apparatus includes a processor configuredto cause retrieval of information regarding a plurality of consumers,and modeling circuitry configured to train the statistical model of theplurality of consumers based on the retrieved information, and predict,using the statistical model, an incremental booking value associatedwith the promotion for each consumer of the plurality of consumers. Theprocessor is further configured to select a subset of the plurality ofconsumers for receiving impressions of the promotion. The apparatus mayfurther include communications circuitry configured to transmit animpression of the promotion to each consumer in the subset of theplurality of consumers.

In some embodiments, the processor is configured to cause retrieval ofinformation regarding the plurality of consumers by causing theapparatus to receive electronic marketing information from one or moreof the plurality of consumers, from one or more merchants, from amemory, or from a combination thereof. In this regard, the electronicmarketing information may include at least one of: a number of purchaseswithin a past thirty days; a number of purchases made by a consumerthrough a promotion and marketing service; a number of impressionsreceived via a consumer's mobile device that have been clicked by theconsumer; a total revenue that a consumer has provided over allpromotions; consumer tenure information; a path that a consumer used tosign up for a promotion and marketing service; a number of impressionsreceived via a consumer's regular email that have been clicked on by theconsumer; an indication of whether a consumer has received an impressionof a particular promotion; a number of promotions purchased by aconsumer within three zip codes of a given location within a priorthirty days; an average cost of each of a consumer's bookings; a numberof active channel subscriptions by a consumer; a total number ofbookings that a consumer has made; a highest price of any promotionpurchased by a consumer via a promotion and marketing service; a numberof promotions between $0 and $15 that have been shown to a consumer; anda median price of a consumer's bookings.

In some embodiments, the statistical model of the plurality of consumerscomprises an ensemble learning model. In some such embodiments, theensemble learning model comprises a gradient boosted regression model.

In some embodiments, the modeling circuitry is configured to predict anexpected incremental booking value of the promotion for each consumer byestimating, using the statistical model, a first expected booking valuethat would be received from the consumer during a first time period inan instance in which the consumer has access to the promotion,estimating, using the statistical model, a second expected booking valuethat would be received from the consumer in an instance in which theconsumer does not have access to the promotion, and calculating a firstdifference value by subtracting the second expected booking value fromthe first expected booking value. In some such embodiments, the expectedincremental booking value may comprise the first difference value. Inother such embodiments, the processor is further configured to calculatea second difference value by subtracting a discount value associatedwith the promotion from the first difference value, wherein the expectedincremental booking value comprises the second difference value.

In some embodiments, the processor is configured to select a subset ofthe plurality of consumers for receiving impressions of the promotion byranking the plurality of consumers by incremental booking valuesassociated with the promotion that correspond to each of the pluralityof consumers, and selecting a predetermined percentage of highest rankedconsumers, wherein the subset of the plurality of consumers to targetcomprises the predetermined percentage of highest ranked consumers.

In some embodiments, selection of the subset of the plurality ofconsumers maximizes expected revenue generated by the promotion.

In another example embodiment, a method is provided for improvedtargeting of a promotion. The method includes retrieving informationregarding a plurality of consumers, training, by modeling circuitry, astatistical model of the plurality of consumers based on the retrievedinformation, predicting, by the modeling circuitry and using thestatistical model, an incremental booking value associated with thepromotion for each consumer of the plurality of consumers, andselecting, by a processor, a subset of the plurality of consumers forreceiving impressions of the promotion. The method may further includecommunications circuitry configured to transmit an impression of thepromotion to each consumer in the subset of the plurality of consumers.

In some embodiments, causing retrieval of information regarding theplurality of consumers includes receiving electronic marketinginformation from one or more of the plurality of consumers, from one ormore merchants, from a memory, or from a combination thereof. In thisregard, the electronic marketing information may include at least oneof: a number of purchases within a past thirty days; a number ofpurchases made by a consumer through a promotion and marketing service;a number of impressions received via a consumer's mobile device thathave been clicked by the consumer; a total revenue that a consumer hasprovided over all promotions; consumer tenure information; a path that aconsumer used to sign up for a promotion and marketing service; a numberof impressions received via a consumer's regular email that have beenclicked on by the consumer; an indication of whether a consumer hasreceived an impression of a particular promotion; a number of promotionspurchased by a consumer within three zip codes of a given locationwithin a prior thirty days; an average cost of each of a consumer'sbookings; a number of active channel subscriptions by a consumer; atotal number of bookings that a consumer has made; a highest price ofany promotion purchased by a consumer via a promotion and marketingservice; a number of promotions between $0 and $15 that have been shownto a consumer; and a median price of a consumer's bookings.

In some embodiments, the statistical model of the plurality of consumerscomprises an ensemble learning model. In some such embodiments, theensemble learning model comprises a gradient boosted regression model.

In some embodiments, the method further includes calculating an expectedincremental booking value of the promotion for each consumer byestimating, using the statistical model, a first expected booking valuethat would be received from the consumer during a first time period inan instance in which the consumer has access to the promotion,estimating, using the statistical model, a second expected booking valuethat would be received from the consumer in an instance in which theconsumer does not have access to the promotion, and calculating a firstdifference value by subtracting the second expected booking value fromthe first expected booking value. In some such embodiments, the expectedincremental booking value may comprise the first difference value. Inother such embodiments, the method further includes calculating a seconddifference value by subtracting a discount value associated with thepromotion from the first difference value, wherein the expectedincremental booking value comprises the second difference value.

In some embodiments, the method further includes selecting a subset ofthe plurality of consumers for receiving impressions of the promotion byranking the plurality of consumers by incremental booking valuesassociated with the promotion that correspond to each of the pluralityof consumers, and selecting a predetermined percentage of highest rankedconsumers, wherein the subset of the plurality of consumers to targetcomprises the predetermined percentage of highest ranked consumers.

In some embodiments, selection of the subset of the plurality ofconsumers maximizes expected revenue generated by the promotion.

In yet another example embodiment, an apparatus is provided for improvedmachine learning using a statistical model to facilitate improvedtargeting of a promotion. The apparatus includes means for retrievinginformation regarding a plurality of consumers, means for training thestatistical model of the plurality of consumers based on the retrievedinformation, means for predicting, using the statistical model, anincremental booking value associated with the promotion for eachconsumer of the plurality of consumers, and means for selecting a subsetof the plurality of consumers for receiving impressions of thepromotion. The apparatus may further include means for transmitting animpression of the promotion to each consumer in the subset of theplurality of consumers.

In some embodiments, the means for causing retrieval of informationregarding the plurality of consumers includes means for causing theapparatus to receive electronic marketing information from one or moreof the plurality of consumers, from one or more merchants, from amemory, or from a combination thereof. In this regard, the electronicmarketing information may include at least one of: a number of purchaseswithin a past thirty days; a number of purchases made by a consumerthrough a promotion and marketing service; a number of impressionsreceived via a consumer's mobile device that have been clicked by theconsumer; a total revenue that a consumer has provided over allpromotions; consumer tenure information; a path that a consumer used tosign up for a promotion and marketing service; a number of impressionsreceived via a consumer's regular email that have been clicked on by theconsumer; an indication of whether a consumer has received an impressionof a particular promotion; a number of promotions purchased by aconsumer within three zip codes of a given location within a priorthirty days; an average cost of each of a consumer's bookings; a numberof active channel subscriptions by a consumer; a total number ofbookings that a consumer has made; a highest price of any promotionpurchased by a consumer via a promotion and marketing service; a numberof promotions between $0 and $15 that have been shown to a consumer; anda median price of a consumer's bookings.

In some embodiments, the statistical model of the plurality of consumerscomprises an ensemble learning model. In some such embodiments, theensemble learning model comprises a gradient boosted regression model.

In some embodiments, the apparatus further includes means forcalculating an expected incremental booking value of the promotion foreach consumer by estimating, using the statistical model, a firstexpected booking value that would be received from the consumer during afirst time period in an instance in which the consumer has access to thepromotion, estimating, using the statistical model, a second expectedbooking value that would be received from the consumer in an instance inwhich the consumer does not have access to the promotion, andcalculating a first difference value by subtracting the second expectedbooking value from the first expected booking value. In some suchembodiments, the expected incremental booking value may comprise thefirst difference value. In other such embodiments, the apparatus furtherincludes means for calculating a second difference value by subtractinga discount value associated with the promotion from the first differencevalue, wherein the expected incremental booking value comprises thesecond difference value.

In some embodiments, the apparatus further includes means for selectinga subset of the plurality of consumers for receiving impressions of thepromotion by ranking the plurality of consumers by incremental bookingvalues associated with the promotion that correspond to each of theplurality of consumers, and selecting a predetermined percentage ofhighest ranked consumers, wherein the subset of the plurality ofconsumers to target comprises the predetermined percentage of highestranked consumers.

In some embodiments, selection of the subset of the plurality ofconsumers maximizes expected revenue generated by the promotion.

In yet another example embodiment, a computer program product isprovided for improved machine learning using a statistical model tofacilitate improved targeting of a promotion. The computer programproduct includes a memory storing computer program instructions that,when executed, cause an apparatus to retrieve information regarding aplurality of consumers, train the statistical model of the plurality ofconsumers based on the retrieved information, predict, using thestatistical model, an incremental booking value associated with thepromotion for each consumer of the plurality of consumers, and select asubset of the plurality of consumers for receiving impressions of thepromotion. The computer program instruction may further be configuredto, when executed, cause the apparatus to transmit an impression of thepromotion to each consumer in the subset of the plurality of consumers.

The above summary is provided merely for purposes of summarizing someexample embodiments to provide a basic understanding of some aspects ofthe invention. Accordingly, it will be appreciated that theabove-described embodiments are merely examples and should not beconstrued to narrow the scope or spirit of the invention in any way. Itwill be appreciated that the scope of the invention encompasses manypotential embodiments in addition to those here summarized, some ofwhich will be further described below.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described certain example embodiments of the presentdisclosure in general terms, reference will now be made to theaccompanying drawings, which are not necessarily drawn to scale, andwherein:

FIG. 1 shows an example system diagram, in accordance with an exampleembodiment of the present invention;

FIG. 2 illustrates a schematic block diagram of circuitry embodying apromotion and marketing service, in accordance with some exampleembodiments;

FIG. 3 illustrates a schematic block diagram of circuitry embodying aconsumer device or a merchant device, in accordance with some exampleembodiments;

FIG. 4 illustrates an example data flow diagram illustratinginteractions between a promotion and marketing service, one or moreconsumer devices, and one or more merchant devices, in accordance withsome example embodiments;

FIG. 5 illustrates a graph showing magnitudes of relative impact of aseries of electronic marketing information, in accordance with someexample embodiments;

FIGS. 6A and 6B illustrate incremental booking values in graph formplotted against different fractions of a rank-ordered consumerpopulation, in accordance with some example embodiments;

FIGS. 7A and 7B illustrate the same fractions of a rank-ordered consumerpopulation graphed against the Contribution to Profit (“CP”) of apromotion, in accordance with some example embodiments;

FIGS. 8A and 8B illustrate the monetary impact of offering promotions todifferent segments of a consumer population, in accordance with someexample embodiments;

FIGS. 9A and 9B illustrate diagrams that show the projected impact ofoffering promotions to different segments of a consumer population, inaccordance with some example embodiments;

FIGS. 10A and 10B illustrate the differential impact on incrementalbooking and CP, respectively, of offering a promotion to a top fractionof consumers in a population and offering the same promotion to a randomfraction of the consumer population, in accordance with some exampleembodiments;

FIG. 11 illustrates a flowchart describing example operations forenhancing the expected revenue derived from a particular promotionaloffering, in accordance with some example embodiments;

FIG. 12 illustrates a flowchart describing example operations forestimating incremental booking values associated with a promotion for aconsumer, in accordance with some example embodiments; and

FIG. 13 illustrates a flowchart describing example operations forselecting consumers to receive impressions of a promotion, in accordancewith some example embodiments.

DETAILED DESCRIPTION Overview

Businesses of all types continue to search for ways to increase revenueand profit. For promotion and marketing services, a basic goal is toidentify promotions that are attractive to consumers. In many cases,however, such promotions offer products or services for which consumershave a preexisting interest. Put another way, while attractivepromotions may amplify consumer interest and likelihood of purchase,some consumers will demonstrate a degree of interest that would lead toa purchase even without promotions.

The implication of this fact is that while providing promotions canincrease sales volume, marketing promotions also carries with it a costin the form of consumers who redeem promotions, but who would otherwisehave made similar purchases. As a result, delivering promotions that areattractive to consumers can, in some situations, actually lead to areduction in organic revenues or profit.

A promotion and marketing service that can differentiate between thoseconsumers who require a promotion to prompt a purchase and those who donot provides a powerful value proposition to merchants. Thisdifferentiation would enable merchants that offer many items at avariety of prices to increase revenue by selectively targetingpromotions to only some users and some items.

Considering the factors above, example embodiments described herein aredesigned to maximize revenue received in response to customerrelationship campaigns run by a promotion and marketing service bycarefully choosing consumers to whom different promotions are offered.The procedures for choosing which promotions to offer to which consumersare based on an architecture that models consumer behavior using a widevariety of factors and predicts an expected incremental booking valuefor a particular promotion and a particular consumer. The factors usedto develop the model may comprise electronic marketing information,which may include information regarding consumer purchase history,consumer activity (e.g., clickstream data), location data, demographicinformation, or the like. By modeling consumer behavior and predictingexpected incremental booking values of a consumer population, exampleembodiments facilitate the delivery of promotions to a subset of theconsumer population that maximize merchants' expected revenues.

Definitions

Some embodiments of the present invention will now be described morefully hereinafter with reference to the accompanying drawings, in whichsome, but not all embodiments of the invention are shown. Indeed, theinvention may be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein; rather, theseembodiments are provided so that this disclosure will satisfy applicablelegal requirements. Like numbers refer to like elements throughout.

As used herein, the terms “data,” “content,” “information,” and similarterms may be used interchangeably to refer to data capable of beingtransmitted, received, and/or stored in accordance with embodiments ofthe present invention. Thus, use of any such terms should not be takento limit the spirit and scope of embodiments of the present invention.Further, where a computing device is described herein to receive datafrom another computing device, it will be appreciated that the data maybe received directly from the another computing device or may bereceived indirectly via one or more intermediary computing devices, suchas, for example, one or more servers, relays, routers, network accesspoints, base stations, hosts, and/or the like, sometimes referred toherein as a “network.” Similarly, where a computing device is describedherein to send data to another computing device, it will be appreciatedthat the data may be sent directly to the another computing device ormay be sent indirectly via one or more intermediary computing devices,such as, for example, one or more servers, relays, routers, networkaccess points, base stations, hosts, and/or the like.

As used herein, the term “promotion and marketing service” may include aservice that is accessible via one or more computing devices and that isoperable to provide promotion and/or marketing services on behalf of oneor more providers that are offering one or more instruments that areredeemable for goods, services, experiences and/or the like. In someexamples, the promotion and marketing service may take the form of aredemption authority, a payment processor, a rewards provider, an entityin a financial network, a promoter, an agent and/or the like. As such,the service is, in some example embodiments, configured to present oneor more promotions via one or more impressions, accept payments forpromotions from consumers, issue instruments upon acceptance of anoffer, participate in redemption, generate rewards, provide a point ofsale device or service, issue payments to providers and/or or otherwiseparticipate in the exchange of goods, services or experiences forcurrency, value and/or the like. The service is also, in some exampleembodiments, configured to offer merchant services such as promotionbuilding (e.g., assisting merchants with selecting parameters for newlycreated promotions), promotion counseling (e.g., offering information tomerchants to assist with using promotions as marketing), promotionanalytics (e.g., offering information to merchants to provide data andanalysis regarding the costs and return-on-investment associated withoffering promotions), and the like.

As used herein, the terms “vendor,” “provider,” and “merchant” may beused interchangeably and may include, but are not limited to, a businessowner, consigner, shopkeeper, tradesperson, operator, entrepreneur,agent, dealer, organization or the like that is in the business of aproviding a good, service or experience to a consumer, facilitating theprovision of a good, service or experience to a consumer and/orotherwise operating in the stream of commerce. The “vendor,” “provider,”or “merchant” need not actually market a product or service via thepromotion and marketing service, and may utilize the promotion andmarketing service only for the purpose of gathering marketinginformation, demographic information, or the like.

As used herein, the term “consumer” should be understood to refer to arecipient of goods, services, promotions, media, or the like provided bythe promotion and marketing service and/or a merchant. Consumers mayinclude, without limitation, individuals, groups of individuals,corporations, other merchants, and the like.

As used herein, the term “promotion” may include, but is not limited to,any type of offered, presented or otherwise indicated reward, discount,coupon, credit, deal, incentive, discount, media or the like that isindicative of a promotional value or the like that upon purchase oracceptance results in the issuance of an instrument that may be usedtoward at least a portion of the purchase of particular goods, servicesand/or experiences defined by the promotion. Promotions may havedifferent values in different contexts. For example, a promotion mayhave a first value associated with the cost paid by a consumer, known asan “accepted value.” When redeemed, the promotion may be used topurchase a “promotional value” representing the retail price of thegoods. The promotion may also have a “residual value,” reflecting theremaining value of the promotion after expiration. Although consumersmay be primarily focused on the accepted and promotional value of thepromotion, a promotion may also have additional associated values. Forexample, a “cost value” may represent the cost to the merchant to offerthe promotion via the promotion and marketing service, where thepromotion and marketing service receives the cost value for eachpromotion sold to a consumer. The promotion may also include a “returnon investment” value, representing a quantified expected return oninvestment to the merchant for each promotion sold.

For example, consider a promotion offered by the promotion and marketingservice for a $50 meal promotion for $25 at a particular restaurant. Inthis example, $25 would be the accepted value charged to the consumer.The consumer would then be able to redeem the promotion at therestaurant for $50 applied toward their meal check. This $50 would bethe promotional value of the promotion. If the consumer did not use thepromotion before expiration, the consumer might be able to obtain arefund of $22.50, representing a 10% fee to recoup transaction costs forthe merchant and/or promotion and marketing service. This $22.50 wouldbe the residual value of the promotion. If the promotion and marketingservice charged the merchant $3.00 to offer the promotion, the $3.00 feewould be the “cost value.” The “return on investment” value of thepromotion might be dynamically calculated by the promotion and marketingservice based on the expected repeat business generated by the marketingof the promotion, the particular location, the demographics of theconsumer, and the like. For example, the return on investment valuemight be $10.00, reflecting the long term additional profit expected bythe merchant as a result of bringing in a new customer through use of apromotion.

Promotions may be provided to consumers and redeemed via the use of an“instrument.” Instruments may represent and embody the terms of thepromotion from which the instrument resulted. For example, instrumentsmay include, but are not limited to, any type of physical token (e.g.,magnetic strip cards or printed barcodes), virtual account balance(e.g., a promotion being associated with a particular user account on amerchant website), secret code (e.g., a character string that can beentered on a merchant website or point-of-sale), tender, electroniccertificate, medium of exchange, voucher, or the like which may be usedin a transaction for at least a portion of the purchase, acquisition,procurement, consumption or the like of goods, services and/orexperiences as defined by the terms of the promotion.

In some examples, the instrument may take the form of tender that has agiven value that is exchangeable for goods, services and/or experiencesand/or a reduction in a purchase price of a particular good, service orexperience. In some examples, the instrument may have multiple values,such as accepted value, a promotional value and/or a residual value. Forexample, using the aforementioned restaurant as the example provider, anelectronic indication in a mobile application that shows $50 of value tobe used as payment for a meal check at the restaurant. In some examples,the accepted value of the instrument is defined by the value exchangedfor the instrument. In some examples, the promotional value is definedby the promotion from which the instrument resulted and is the value ofthe instrument beyond the accepted value. In some examples, the residualvalue is the value after redemption, the value after the expiry or otherviolation of a redemption parameter, the return or exchange value of theinstrument and/or the like.

As used herein, the term “redemption” refers to the use, exchange orother presentation of an instrument for at least a portion of a good,service or experience as defined by the instrument and its relatedpromotion. In some examples, redemption includes the verification ofvalidity of the instrument. In other example embodiments, redemption mayinclude an indication that a particular instrument has been redeemed andthus no longer retains an actual, promotional and/or residual value(e.g., full redemption). In other example embodiments, redemption mayinclude the redemption of at least a portion of its actual, promotionaland/or residual value (e.g., partial redemption). An example ofredemption, using the aforementioned restaurant as the example provider,is the exchange of the $50 instrument and $50 to settle a $100 mealcheck.

As used herein, the term “impression” refers to a metric for measuringhow frequently consumers are provided with marketing information relatedto a particular good, service, or promotion. Impressions may be measuredin various different manners, including, but not limited to, measuringthe frequency with which content is served to a consumer (e.g., thenumber of times images, websites, or the like are requested byconsumers), measuring the frequency with which electronic marketingcommunications including particular content are sent to consumers (e.g.,a number of e-mails sent to consumers or number of e-mails includingparticular promotion content), measuring the frequency with whichelectronic marketing communications are received by consumers (e.g., anumber of times a particular e-mail is read), or the like. Impressionsmay be provided through various forms of media, including but notlimited to communications, displays, or other perceived indications,such as e-mails, text messages, application alerts, mobile applications,other type of electronic interface or distribution channel and/or thelike, of one or more promotions.

As used herein, the term “electronic marketing information” refers tothe subset of types of electronic data and signals that may beinterpreted by a promotion and marketing service to provide improvedelectronic marketing communications. Electronic marketing informationmay include, without limitation, clickstream data (defined below),transaction data (defined below), location data (defined below), contextinformation (defined below), communication channel data (defined below),discretionary data (defined below), or any other data stored by orreceived by the promotion and marketing service for use in providingelectronic communications to consumers.

As used herein, the term “clickstream data” refers to electronicinformation indicating content viewed, accessed, edited, or retrieved byconsumers. This information may be electronically processed and analyzedby a promotion and marketing service to improve the quality ofelectronic marketing and commerce transactions offered by, through, andin conjunction with the promotion and marketing service. It should beunderstood that the term “clickstream” is not intended to be limited tomouse clicks. For example, the clickstream data may include variousother consumer interactions, including without limitation, mouse-overevents and durations, the amount of time spent by the consumer viewingparticular content, the rate at which impressions of particular contentresult in sales associated with that content, demographic informationassociated with each particular consumer, data indicating other contentaccessed by the consumer (e.g., browser cookie data), the time or dateon which content was accessed, the frequency of impressions forparticular content, associations between particular consumers orconsumer demographics and particular impressions, and/or the like.

As used herein, the term “transaction data” refers to electronicinformation indicating that a transaction is occurring or has occurredvia either a merchant or the promotion and marketing service.Transaction data may also include information relating to thetransaction. For example, transaction data may include consumer paymentor billing information, consumer shipping information, items purchasedby the consumer, a merchant rewards account number associated with theconsumer, the type of shipping selected by the consumer for fulfillmentof the transaction, or the like.

As used herein, the term “location data” refers to electronicinformation indicating a particular location. Location data may beassociated with a consumer, a merchant, or any other entity capable ofinteraction with the promotion and marketing service. For example, insome embodiments location data is provided by a location services moduleof a consumer mobile device. In some embodiments, location data may beprovided by a merchant indicating the location of consumers within theirretail location. In some embodiments, location data may be provided bymerchants to indicate the current location of the merchant (e.g., a foodtruck or delivery service). It should be appreciated that location datamay be provided by various systems capable of determining locationinformation, including, but not limited to, global positioning service(GPS) receivers, indoor navigation systems, cellular tower triangulationtechniques, video surveillance systems, or radio frequencyidentification (RFID) location systems.

As used herein, the term “communication channel data” refers toelectronic information relating to the particular device orcommunication channel upon which a merchant or consumer communicateswith the promotion and marketing service. In this regard, communicationchannel data may include the type of device used by the consumer ormerchant (e.g., smart phone, desktop computer, laptop, netbook, tabletcomputer), the Internet Protocol (IP) address of the device, theavailable bandwidth of a connection, login credentials used to accessthe channel (e.g., a user account and/or password for accessing thepromotion and marketing service), or any other data pertaining to thecommunication channel between the promotion and marketing service and anentity external to the promotion and marketing service.

As used herein, the term “discretionary data” refers to electronicinformation provided by a merchant or consumer explicitly to thepromotion and marketing service in support of improved interaction withthe promotion and marketing service. Upon registering with the promotionand marketing service or at any time thereafter, the consumer ormerchant may be invited to provide information that aids the promotionand marketing service in providing services that are targeted to theparticular needs of the consumer or merchant. For example, a consumermay indicate interests, hobbies, their age, gender, or location whencreating a new account. A merchant may indicate the type of goods orservices provided, their retail storefront location, contactinformation, hours of operation, or the like.

It should be appreciated that the term “discretionary data” is intendedto refer to information voluntarily and explicitly provided to thepromotion and marketing service, such as by completing a form or surveyon a website or application hosted by the promotion and marketingservice. However, is should be appreciated that the examples ofdiscretionary data provided above may also be determined implicitly orthrough review or analysis of other electronic marketing informationprovided to the promotion and marketing service. It should also beappreciated that the promotion and marketing service may also gateaccess to certain features or tools based on whether certaindiscretionary data has been provided. For example, the consumer may berequired to provide information relating to their interests or locationduring a registration process.

As used herein, the term “offering parameters” refers to terms andconditions under which the promotion is offered by a promotion andmarketing service to consumers. These offering parameters may includeparameters, bounds, considerations and/or the like that outline orotherwise define the terms, timing, constraints, limitations, rules orthe like under which the promotion is sold, offered, marketed, orotherwise provided to consumers. Example offering parameters include,using the aforementioned restaurant as the example provider, limit oneinstrument per person, total of 100 instruments to be issued, a runduration of when the promotion will be marketed via the promotion andmarketing service, and parameters for identifying consumers to beoffered the promotion (e.g., factors influencing how consumer locationsare used to offer a promotion).

As used herein, the term “redemption parameters” refers to terms andconditions for redeeming or otherwise obtaining the benefit ofpromotions obtained from a promotion and marketing service. Theredemption parameters may include parameters, bounds, considerationsand/or the like that outline the term, timing, constraints, limitations,rules or the like for how and/or when an instrument may be redeemed. Forexample, the redemption parameters may include an indication that theinstrument must be redeemed prior to a specified deadline, for aspecific good, service or experience and/or the like. For example, usingthe aforementioned restaurant as the example provider, the redemptionparameters may specify a limit of one instrument per visit, that thepromotion must be used in store only, or that the promotion must be usedby a certain date.

As used herein, the term “promotion content” refers to display factorsor features that influence how the promotion is displayed to consumers.For example, promotion content may include an image associated with thepromotion, a narrative description of the promotion or the merchant, adisplay template for association with the promotion, or the like. Forexample, merchant self-service indicators (defined below) may be used toidentify promotion offers that were generated by merchants with similarcharacteristics to the merchant self-service indicators. Various otherfactors may be used to generate the promotion offer, such as the successof the promotion offers generated by the merchants with similarcharacteristics, the product availability of the merchant, and the like.

As used herein, the term “promotion component” is used to refer toelements of a particular promotion that may be selected during apromotion generation process. Promotion components may include anyaspect of a promotion, including but not necessarily limited to offeringparameters, redemption parameters, and promotion content. For example,promotion components may include, but are not limited to, promotiontitles, promotion ledes (e.g., a short text phrase displayed under apromotion title), promotion images, promotion prices, promotion discountlevels, promotion style sheets, promotion fonts, promotion e-mailsubjects, promotion quantities, promotion fine print options, promotionfees assessed to the merchant by the promotion and marketing service, orthe like. Promotion components may also include various flags andsettings associated with registration and verification functions for amerchant offering the promotion, such as whether the identity of themerchant has been verified, whether the merchant is registered with thepromotion and marketing service, or the like.

As used herein, the term “electronic marketing communication” refers toany electronically generated information content provided by thepromotion and marketing service or a merchant and to a consumer for thepurpose of marketing a promotion, good, or service to the consumer.Electronic marketing communications may include any email, short messageservice (SMS) message, web page, application interface, or the likeelectronically generated for the purpose of attempting to sell or raiseawareness of a product, service, promotion, or merchant to the consumer.

It should be appreciated that the term “electronic marketingcommunication” implies and requires some portion of the content of thecommunication to be generated via an electronic process. For example, atelephone call made from an employee of the promotion and marketingservice to a consumer for the purpose of selling a product or servicewould not qualify as an electronic marketing communication, even if theidentity of the call recipient was selected by an electronic process andthe call was dialed electronically, as the content of the telephone callis not generated in an electronic manner. However, a so-called“robo-call” with content programmatically selected, generated, orrecorded via an electronic process and initiated by an electronic systemto notify a consumer of a particular product, service, or promotionwould qualify as an electronic marketing communication. Similarly, amanually drafted e-mail sent from an employee of the promotion andmarketing service to a consumer for the purpose of marketing a productwould not qualify as an electronic marketing communication. However, aprogrammatically generated email including marketing materialsprogrammatically selected based on electronic marketing informationassociated with the recipient would qualify as an electronic marketingcommunication.

As used herein, the term “business analytic data” refers to datagenerated by the promotion and marketing service based on electronicmarketing information to assist with the operation of the promotion andmarketing service and/or one or more merchants. The various streams ofelectronic marketing information provided to and by the promotion andmarketing service allow for the use of sophisticated data analysistechniques that may be employed to identify correlations, relationships,and other associations among elements of electronic marketinginformation. These associations may be processed and formatted by thepromotion and marketing service to provide reports, recommendations, andservices both internal to the promotion and marketing service and tomerchants in order to improve the process by which merchants andpromotion and marketing service engage with consumers. For example, thepromotion and marketing service may analyze the electronic marketinginformation to identify an increased demand for a particular product orservice, and provide an electronic report to a merchant suggesting themerchant offer the particular product or service. Alternatively, thepromotion and marketing service may identify that a particular productor service is not selling well or that sales of the product or serviceresult in the merchant losing money, customers, or market share (e.g.,after consumers order a particular menu item, they never come back tothe merchant), and suggest that the merchant should discontinue offeringthat product or service.

It should be appreciated that the term “business analytic data” isintended to refer to electronically and programmatically generated data.For example, a printed report or letter manually drafted by an employeeof the promotion and marketing service would not be said to includebusiness analytic data, even if said data was used by the employeeduring the drafting process, while a data disk or downloaded filecontaining analytics generated by the promotion and marketing servicewould be considered business analytic data.

It should be appreciated that the term “incremental booking value” isintended to refer to the difference between the revenue generated by aconsumer who is offered a promotion during a particular time period andthe revenue generated by the consumer when not offered the promotionduring the particular time period. For example, during a promotioncampaign, if a consumer would redeem goods, services and/or experiencesworth $30 when provided with a particular promotion that offers adiscount of $10, but would only purchases goods, services and/orexperiences worth $15 when not provided with the particular promotion,the consumer's incremental booking value for the particular promotion isthe difference in revenue generated between the two scenarios. The firstscenario generates revenue of $30 at a cost of $10, for a total revenuegeneration of $20. The second scenario generates revenue of $15 with nocost. Accordingly, the incremental booking value for the consumer forthe particular promotion is $5 ($30-$15-$10).

It should be understood that the incremental booking values of apopulation may be modeled using electronic marketing information. Inthis regard, a model can be trained, using electronic marketinginformation, to predict a consumer's expected incremental booking valuefor promotions that will be offered in the future. While the actualincremental booking value of any particular consumer cannot becalculated (each consumer either utilizes a promotion or does notutilize a promotion, so a single consumer never does both in aparticular time period). However, the incremental booking value for aparticular consumer can be estimated based on a comparison of theparticular consumer's own behavior and the behavior of similar consumers(all of which may be captured as electronic marketing information).

It should be appreciated that the term “identifier entity” is intendedto refer to a consumer profile. A database of identifier entities may begenerated and created by the promotion and marketing service or, in someembodiments, by individual merchants. In either case, an identifierentity represents a specific consumer and is associated with one or moreconsumer devices (e.g., a smartphone, one or more desktop devices, suchas a work computer and a home computer, and/or the like) used by theconsumer to access the promotion and marketing service (or merchant).

Technical Underpinnings and Implementation of Exemplary Embodiments

Merchants, including manufacturers, wholesalers, and retailers, havespent a tremendous amount of time, money, manpower, and other resourcesto determine the best way to market their products to consumers. Whethera given marketing effort is successful is often determined based on thereturn-on-investment offered to the merchant from increased awareness,sales, and the like of the merchant's goods and services in exchange forthe resources spent on the marketing effort. In other words, optimalmarketing techniques generally maximize the benefit to the merchant'sbottom line while minimizing the cost spent on marketing. To this end, amerchant's marketing budget may be spent in a variety of differentmanners including advertising, offering of discounts, conducting marketresearch, and various other known marketing techniques. The end goal ofthese activities is to ensure that products are presented to consumersin a manner that maximizes the likelihood that the consumers willpurchase the product from the merchant that performed the marketingactivities while minimizing the expense of the marketing effort.

The advent of electronic commerce has revolutionized the marketingprocess. While merchants would typically have to perform costly marketresearch such as focus groups, surveys, and the like to obtain detailedinformation on consumer preferences and demographics, the digital agehas provided a wealth of new consumer information that may be used tooptimize the marketing and sales process. As a result, new technologieshave been developed to gather, aggregate, analyze, and reportinformation from a variety of electronic sources.

So-called “clickstream data” provides a robust set of informationdescribing the various interactions consumers have with electronicmarketing information provided to them by merchants and others.Promotion and marketing services have been developed with sophisticatedtechnology to receive and process this and other types of data(collectively, electronic marketing information, defined above) for thebenefit of both merchants and consumers. Promotion and marketingservices assist merchants with marketing their products to interestedconsumers, while reducing the chance that a consumer will be presentedwith marketing information in which the consumer has no interest. Somepromotion and marketing services further leverage their access to thetrove of electronic marketing information to assist merchants andconsumers with other tasks, such as offering improved merchantpoint-of-sale systems, improved inventory and supply chain management,improved methods for delivering products and services, and the like.

Unlike conventional marketing techniques related to the use of paper orother physical media (e.g., coupons clipped from a weekly newspaper),the ability of a promotion and marketing services to capture electronicmarketing information offers a wealth of additional electronic solutionsto improve the experience for consumers and merchants. For instance, theability to closely monitor user impressions enables the promotion andmarketing service to gather data related to the time, place, and mannerin which the consumer engaged with the impression (e.g., viewed,clicked, moused-over) and obtained and redeemed the correspondingpromotion. The promotion and marketing service may use this informationto determine which products and services are most relevant to theconsumer's interest, and to provide marketing materials related to saidproducts and services to the consumer, thus improving the quality of theelectronic marketing communications received by the consumer.

Merchants may be provided with the ability to dynamically monitor andadjust the parameters of promotions offered by the promotion andmarketing service, ensuring that the merchant receives a positive returnon their investment. For example, the merchant can closely monitor thetype, discount level, and quantity sold of a particular promotion on thefly, while with traditional printed coupons the merchant would not beable to make any changes to the promotion after the coupon has gone toprint. Each of these advancements in digital market and promotiondistribution provide additional layers of electronic marketinginformation not before seen in traditional print or television broadcastmarketing.

However, the offerings of modern promotion and marketing services arenot without problems. Although electronic marketing information providesa wealth of information, the inventors have determined that existingtechniques may not always leverage this information in an efficient oraccurate manner. Technology continues to rapidly advance in the field ofelectronic commerce, and as a result electronic marketing servicescontinue to provide new and improved methods for engaging with consumersand offering promotions. In many cases, the inventors have determinedthat these offerings are constrained by technological obstacles uniqueto the electronic nature of the services provided.

For instance, due to the increasing ease with which consumers can accesspromotions offered by electronic marketing services, consumers areincreasingly able to utilize promotions for products that they wouldhave purchased anyway. In such situations, an electronic marketingservice is, in some sense, performing a disservice to merchants, asrevenue from willing purchasers is foregone in favor of promotionaldiscounts that the willing purchasers would likely not have availedthemselves of prior to the ubiquity of online promotion offerings.

The inventors have determined that even technological methods thatleverage computers for statistical analysis and consumer behaviormodeling (e.g., television rating systems) fail to address problemsassociated with providing electronic marketing communications (e.g.,impressions) to consumers in a manner that provides for efficientallocation of resources. However, by collecting greater amounts ofelectronic marketing information, the inventors have determined thatincreased consumer modeling can increase the efficiency of offeringpromotions. Example embodiments described herein serve to address theseand other deficiencies by offering utilizing electronic marketinginformation to enhance promotion targeting efforts in a way that wouldbe unavailable without the quality and breadth of electronic data madeavailable by the electronic nature of the service data flow (describedbelow).

Accordingly, various example embodiments provide systems that modelhistorical consumer behavior and predict expected incremental bookingvalues of various promotions for various consumers. Such embodimentsthus avoid hurdles imposed by the proliferation of promotions toconsumers who would otherwise purchase goods, services or experiencesanyway. Example embodiments described herein thus improve revenuegenerating potential of promotions offered by a promotion and marketingservice, and in turn improve the value proposition offered by thepromotion and marketing service.

System Architecture

Methods, apparatuses, and computer program products of the presentinvention may be embodied by any of a variety of devices. For example,the method, apparatus, and computer program product of an exampleembodiment may be embodied by a networked device, such as a server orother network entity, configured to communicate with one or moredevices, such as one or more consumer or merchant devices. The computingdevice may include one or more fixed computing devices, such as personalcomputers or computer workstations. Still further, example embodimentsmay be embodied by any of a variety of mobile terminals, such as aportable digital assistant (PDA), mobile telephone, smartphone, laptopcomputer, tablet computer, or any combination of the aforementioneddevices.

In this regard, FIG. 1 discloses an example computing system withinwhich embodiments of the present invention may operate. Consumers andmerchants may access a promotion and marketing service 102 via a network108 (e.g., the Internet, or the like) using consumer devices 110Athrough 110N and merchant devices 112A through 112N, respectively. Whileit is expected that at least one consumer and at least one merchantinteract in the example computing system 100, varying embodimentscontemplate any number of consumers and merchants interacting in thesystem via corresponding consumer terminals 110 and merchant devices112.

The promotion and marketing service 102 may comprise a server 104 incommunication with a database 106. In this regard, the server 104 may beembodied as a computer or computers as known in the art. The server 104may collect electronic marketing information from various sources,including but not necessarily limited to the consumer devices 110Athrough 110N and merchant devices 112A through 112N. For example, theserver 104 may be operable to receive and process clickstream data orcontext information provided by a consumer device 110 and/or otherdevices. The server 104 may also facilitate e-commerce transactionsbased on transaction information provided by a consumer device 110and/or a merchant device 112. The server 104 may facilitate thegeneration and providing of various electronic communications andmarketing materials based on the received electronic marketinginformation, as will be described in greater detail below.

The database 106 may be embodied as a data storage device such as aNetwork Attached Storage (NAS) device or devices, or as a separatedatabase server or servers. The database 106 includes informationaccessed and stored by the server 104 to facilitate the operations ofthe promotion and marketing service 102. For example, the database 106may include user account credentials for merchants, and consumers, dataindicating the products and promotions offered by the promotion andmarketing service, electronic marketing information (e.g., clickstreamdata, transaction data, location data, communication channel data, ordiscretionary data), analytic results, reports, financial data, and/orthe like. As it relates to the example embodiments described herein thedatabase 106 may store a model developed by the promotion and marketingservice 102 to predict the expected booking value of various promotionsfor various consumers.

Each consumer device 110 may be embodied by any computing device knownin the art. Information received by the server 104 from the consumerdevice 110 may be provided in various forms and via various methods. Forexample, the consumer device 110 may include laptop computers,smartphones, netbooks, tablet computers, wearable devices, or the like.The information may be provided through various sources on theseconsumer devices.

In embodiments where a consumer device 110 is a mobile device, such as asmart phone or tablet, the mobile device may execute an “app” tointeract with the promotion and marketing service 102 and/or themerchant devices 112A through 112N. Such apps are typically designed toexecute on mobile devices, such as tablets or smartphones. For example,an app may be provided that executes on mobile device operating systemssuch as Apple Inc.'s iOS®, Google Inc.'s Android®, or Microsoft Inc.'sWindows 8®. These platforms typically provide frameworks that allow appsto communicate with one another and with particular hardware andsoftware components of mobile devices. For example, the mobile operatingsystems named above each provide frameworks for interacting withlocation services circuitry, wired and wireless network interfaces, usercontacts, and other applications in a manner that allows for improvedinteractions between apps while also preserving the privacy and securityof individual users. In some embodiments, a mobile operating system mayalso provide for improved communication interfaces for interacting withexternal devices (e.g., home automation systems, indoor navigationsystems, and the like). Communication with hardware and software modulesexecuting outside of the app is typically provided via applicationprogramming interfaces (APIs) provided by the mobile device operatingsystem.

In the case of a consumer device 110, the promotion and marketing 102may leverage the application framework offered by the mobile operatingsystem to allow consumers to designate which information is harvested bythe app and which may then be provided to the promotion and marketing102. In some embodiments, consumers may “opt in” to provide particulardifferent types of contextual data in exchange for a benefit, such asimproved relevance of marketing communications offered to the consumer.In some embodiments, the consumer may be provided with privacyinformation and other terms and conditions related to the electronicmarketing information harvested by the consumer device 110 and providedto the promotion and marketing 102 during installation or use of theapp. Once the consumer provides access to a particular feature of theconsumer device 110, information derived from that feature may in someembodiments be provided to the promotion and marketing 102 to improvethe quality of the consumer's interactions with the promotion andmarketing 102 and/or merchant devices 112A through 112N.

For example, the consumer may indicate a desire to provide locationinformation to the app from location services circuitry included intheir mobile device. Providing this information to the promotion andmarketing 102 may enable the promotion and marketing 102 to offerpromotions to the consumer that are relevant to the particular locationof the consumer (e.g., by providing promotions for merchants proximateto the consumer's current location). It should be appreciated that thevarious mobile device operating systems may provide the ability toregulate the information provided to the app associated with thepromotion and marketing 102. For example, the consumer may decide at alater point to disable the ability of the app to access the locationservices circuitry, thus limiting the access of the consumer's locationinformation to the backend server 102.

Various other types of information may also be provided in conjunctionwith an app executing on the consumer's mobile device. For example, ifthe mobile device includes a social networking feature, the consumer mayenable the app to provide updates to the consumer's social network tonotify friends of a particularly interesting promotion. It should beappreciated that the use of mobile technology and associated appframeworks may provide for particularly unique and beneficial uses ofthe promotion and marketing service 102 through leveraging thefunctionality offered by the various mobile operating systems.

Additionally or alternatively, each consumer device 110 may interactwith the promotion and marketing 102 or merchant devices 112A through112N via a web browser. As yet another example, each consumer device 110may include various hardware or firmware designed to interface with thepromotion and marketing 102 or merchant devices 112A through 112N (e.g.,where the consumer device 110 is a purpose-built device offered for theprimary purpose of communicating with the promotion and marketingservice 102).

The merchant devices 112A through 112N may be embodied by any computingdevice as known in the art and operated by a merchant. For example, themerchant devices 112A through 112N may include a merchant point-of-sale,a merchant e-commerce server, a merchant inventory system, or acomputing device accessing a web site designed to provide merchantaccess (e.g., by accessing a web page via a browser using a set ofmerchant account credentials). Electronic data received by the promotionand marketing service 102 from the merchant devices 112A through 112Nmay also be provided in various forms and via various methods. Forexample, the merchant devices 112A through 112N may provide real-timetransaction and/or inventory information as purchases are made from themerchant. In other embodiments, the merchant devices 112A through 112Nmay be employed to provide information to the promotion and marketingservice 102 to enable the promotion and marketing service 102 togenerate promotions or other marketing information to be provided toconsumers.

An example of a data flow for exchanging electronic information amongone or more consumer devices 110A through 110N, one or more merchantdevices 112A through 112N, and the promotion and marketing service 102is described below in connection with FIG. 4.

Example Implementing Apparatuses

The server 104 may be embodied by one or more computing systems, such asapparatus 200 shown in FIG. 2. As illustrated in FIG. 2, the apparatus200 may include a processor 202, a memory 204, input/output circuitry206, communications circuitry 208 and modeling circuitry 210. Theapparatus 200 may be configured to execute the operations describedabove with respect to FIG. 1 and below with respect to FIGS. 3 and 11through 13. Although the descriptions of components 202 through 210 mayutilize functional limitations, it should be understood that theparticular implementations necessarily include the use of particularhardware. It should also be understood that certain of these components202 through 210 may include similar or common hardware. For example, twosets of circuitry may both leverage use of the same processor, networkinterface, storage medium, or the like to perform their associatedfunctions, such that duplicate hardware is not required for each set ofcircuitry. The use of the term “circuitry” as used herein with respectto components of the apparatus should therefore be understood to includeparticular hardware configured to perform the functions associated withthe particular circuitry as described herein.

Of course, while the term “circuitry” should be understood broadly toinclude hardware, in some embodiments, circuitry may also includesoftware for configuring the hardware. For example, in some embodiments,“circuitry” may include processing circuitry, storage media, networkinterfaces, input/output devices, and the like. In some embodiments,other elements of the apparatus 200 may provide or supplement thefunctionality of particular circuitry. For example, the processor 202may provide processing functionality, the memory 204 may provide storagefunctionality, the communications circuitry 208 may provide networkinterface functionality, and the like.

In some embodiments, the processor 202 (and/or co-processor or any otherprocessing circuitry assisting or otherwise associated with theprocessor) may be in communication with the memory 204 via a bus forpassing information among components of the apparatus. The memory 204may be non-transitory and may include, for example, one or more volatileand/or non-volatile memories. In other words, for example, the memorymay be an electronic storage device (e.g., a computer readable storagemedium). The memory 204 may be configured to store information, data,content, applications, instructions, or the like, for enabling theapparatus to carry out various functions in accordance with exampleembodiments of the present invention.

The processor 202 may be embodied in a number of different ways and may,for example, include one or more processing devices configured toperform independently. Additionally or alternatively, the processor mayinclude one or more processors configured in tandem via a bus to enableindependent execution of instructions, pipelining, and/ormultithreading. The use of the term “processing circuitry” may beunderstood to include a single core processor, a multi-core processor,multiple processors internal to the apparatus, and/or remote or “cloud”processors.

In an example embodiment, the processor 202 may be configured to executeinstructions stored in the memory 204 or otherwise accessible to theprocessor. Alternatively or additionally, the processor may beconfigured to execute hard-coded functionality. As such, whetherconfigured by hardware or software methods, or by a combination ofhardware with software, the processor may represent an entity (e.g.,physically embodied in circuitry) capable of performing operationsaccording to an embodiment of the present invention while configuredaccordingly. Alternatively, as another example, when the processor isembodied as an executor of software instructions, the instructions mayspecifically configure the processor to perform the algorithms and/oroperations described herein when the instructions are executed.

In some embodiments, the apparatus 200 may include input/outputcircuitry 206 that may, in turn, be in communication with processor 202to provide output to the user and, in some embodiments, to receive anindication of a user input. The input/output circuitry 206 may comprisea user interface and may include a display that may include a web userinterface, a mobile application, a client device, or the like. In someembodiments, the input/output circuitry 206 may also include a keyboard,a mouse, a joystick, a touch screen, touch areas, soft keys, amicrophone, a speaker, or other input/output mechanisms. The processorand/or user interface circuitry comprising the processor may beconfigured to control one or more functions of one or more userinterface elements through computer program instructions (e.g., softwareand/or firmware) stored on a memory accessible to the processor (e.g.,memory 204, and/or the like).

The communications circuitry 208 may be any means such as a device orcircuitry embodied in either hardware or a combination of hardware andsoftware that is configured to receive and/or transmit data from/to anetwork and/or any other device, circuitry, or module in communicationwith the apparatus 200. In this regard, the communications circuitry 208may include, for example, a network interface for enablingcommunications with a wired or wireless communication network. Forexample, the communications circuitry 208 may include one or morenetwork interface cards, antennae, buses, switches, routers, modems, andsupporting hardware and/or software, or any other device suitable forenabling communications via a network. Additionally or alternatively,the communication interface may include the circuitry for interactingwith the antenna(s) to cause transmission of signals via the antenna(s)or to handle receipt of signals received via the antenna(s). Thesesignals may be transmitted by the apparatus 200 using any of a number ofwireless personal area network (PAN) technologies, such as Bluetooth®v1.0 through v3.0, Bluetooth Low Energy (BLE), infrared wireless (e.g.,IrDA), ultra-wideband (UWB), induction wireless transmission, or thelike. In addition, it should be understood that these signals may betransmitted using Wi-Fi, Near Field Communications (NFC), WorldwideInteroperability for Microwave Access (WiMAX) or other proximity-basedcommunications protocols.

Modeling circuitry 210 includes hardware components designed togenerate, train, and maintain one or more consumer models and predict anexpected incremental booking values associated with various promotionsfor various consumers. These hardware components may, for instance,utilize communications circuitry 208 to retrieve stored data fromdatabase 106 and/or receive electronic marketing information from remotedevices (e.g., consumer devices 110, merchant devices 112, or the like).Modeling circuitry 210 may utilize processing circuitry, such as theprocessor 202, to perform the above operations, and may utilize memory204 to store the collected information and any generated consumermodels. It should also be appreciated that, in some embodiments, themodeling circuitry 210 may include a separate processor, speciallyconfigured field programmable gate array (FPGA), or application specificinterface circuit (ASIC) to perform the above functions.

As will be appreciated, any such computer program instructions and/orother type of code may be loaded onto a computer, processor or otherprogrammable apparatus's circuitry to produce a machine, such that thecomputer, processor other programmable circuitry that execute the codeon the machine create the means for implementing various functions,including those described herein.

As described above and as will be appreciated based on this disclosure,embodiments of the present invention may be configured as systems,methods, mobile devices, backend network devices, and the like.Accordingly, embodiments may comprise various means including entirelyof hardware or any combination of software and hardware. Furthermore,embodiments may take the form of a computer program product on at leastone non-transitory computer-readable storage medium havingcomputer-readable program instructions (e.g., computer software)embodied in the storage medium. Any suitable computer-readable storagemedium may be utilized including non-transitory hard disks, CD-ROMs,flash memory, optical storage devices, or magnetic storage devices.

The consumer device(s) 110A through 110N and merchant devices 112Athrough 112N may be embodied by one or more computing systems, such asapparatus 300 shown in FIG. 3. As illustrated in FIG. 3, the apparatus300 may include a processor 302, a memory 304, an input/output circuitry306, and a communications circuitry 308. The apparatus 300 may beconfigured to execute the operations described above with respect toFIG. 1. The functioning of the processor 302, the memory 304, theinput/output circuitry 306, and the communication circuitry 308 may besimilar to the similarly named components described above with respectto FIG. 2. For the sake of brevity, additional description of thesecomponents is omitted.

Having described specific hardware components of example devicesutilized herein, an example of a data flow for exchanging electronicinformation among one or more consumer devices, merchant devices, andthe promotion and marketing service is described below with respect toFIG. 4.

Example Service Data Flow

FIG. 4 depicts an example data flow 400 illustrating interactionsbetween a server 402, one or more consumer devices 404, and one or moremerchant devices 406. The server 402 may be implemented in the same or asimilar fashion as the server 104 as described above with respect toFIG. 1, the one or more consumer devices 404 may be implemented in thesame or a similar fashion as the consumer devices 110A through 110N asdescribed above with respect to FIG. 1, and the one or more merchantdevices 406 may be implemented in the same or a similar fashion as themerchant devices 112A through 112N as described above with respect toFIG. 1.

The data flow 400 illustrates how electronic information may be passedamong various systems when employing a server 402 in accordance withembodiments of the present invention. The one or more consumer devices404 and/or one or more merchant devices 406 may provide a variety ofelectronic marketing information to the server 402 for use in providingpromotion and marketing services to the consumer. This electronicmarketing information may include, but is not limited to, location data,clickstream data, transaction data, communication channel data, and/ordiscretionary data.

As a result of transactions performed between the one or more consumerdevices 404 and the server 402, the server 402 may provide fulfillmentdata to the consumer devices 404. The fulfillment data may includeinformation indicating whether the transaction was successful, thelocation and time the product will be provided to the consumer,instruments for redeeming promotions purchased by the consumer, or thelike.

In addition to the e-commerce interactions with the one or more consumerdevices 404 offered by the server 402, the server 402 may leverageinformation provided by the consumer devices 404 to improve therelevancy of marketing communications to individual consumers or groupsof consumers. In this manner, the server 402 may determine promotions,goods, and services that are more likely to be of interest to aparticular consumer or group of consumers based on the receivedelectronic marketing information. For example, the server 402 may detectthe location of a consumer based on location data provided by theconsumer device, and offer promotions based on the proximity of theconsumer to the merchant associated with those promotions.

It should be appreciated that a variety of different types of electronicmarketing information could be provided to the server 402 for thepurpose of improving the relevancy of marketing communications. Itshould also be appreciated that this electronic marketing informationmay be received from a variety of electronic sources, including variousconsumer devices, merchant devices, and other sources both internal andexternal to a promotion and marketing service. For example, other datasources may include imported contact databases maintained by merchants,electronic survey questions answered by consumers, and/or various otherforms of electronic data.

It should also be appreciated that the server 402 may also control otherfactors of the electronic marketing communications sent to the consumerother than the particular promotions included in the electronicmarketing communication. For example, the server 402 may determine theform, structure, frequency, and type of the electronic marketingcommunication. As with the content of the electronic marketingcommunication, these factors may be programmatically determinedaccording to various methods, factors, and processes based on electronicdata received by the server 402.

The server 402 interactions with the one or more merchant devices 406may be related to enabling the merchant to market their products using apromotion and marketing service. For example, the one or more merchantdevices 406 may provide promotion data defining one or more promotionsto be offered by the promotion and marketing service on behalf of themerchant. The server 402 may receive this information and generateinformation for providing such promotions via an e-commerce interface,making the promotions available for purchase by consumers. The server402 may also receive information about products from the one or moremerchant devices 406. For example, a merchant may provide electronicmarketing information indicating particular products, product prices,inventory levels, and the like to be marketed via a promotion andmarketing service. The server 402 may receive this information andgenerate listing information to offer the indicating products toconsumers via a promotion and marketing service.

The one or more merchant devices 406 may also receive information fromthe server 402. For example, in some embodiments a merchant may obtainaccess to certain business analytic data aggregated, generated, ormaintained by the server 402. As a particular example, a merchant mightoffer to pay for consumer demographic data related to products orservices offered by the merchant. It should be appreciated however, thata merchant may not need to list any products or services via thepromotion and marketing service in order to obtain such data. Forexample, the promotion and marketing service may enable merchants toaccess electronic marketing data offered via the promotion and marketingservice based on a subscription model. The one or more merchant devices406 may also receive electronic compensation data from the server 402.For example, when a promotion or product is sold by the promotion andmarketing service on behalf of the merchant, a portion of the receivedfunds may be transmitted to the merchant. The compensation data mayinclude information sufficient to notify the merchant that such fundsare being or have been transmitted. In some embodiments, thecompensation data may take the form of an electronic wire transferdirectly to a merchant account. In some other embodiments, thecompensation data may indicate that a promotion or product has beenpurchased, but the actual transfer of funds may occur at a later time.For example, in some embodiments, compensation data indicating the saleof a promotion may be provided immediately, but funds may not betransferred to the merchant until the promotion is redeemed by theconsumer.

Moreover, it should be understood that the server 402 may train a modelto predict expected incremental booking values that illustrate theadditional value added by offering promotions to consumers. The expectedincremental booking values can reveal categories of consumers thatshould receive each promotion (and those that shouldn't). The expectedincremental booking values need not be transmitted by the server 404 toanother device, but in some embodiments the incremental booking valuesmay be transmitted to the merchant devices 406 for separate reviewand/or analysis.

Embodiments advantageously provide for improvements to the server and/ormerchant devices by improving the efficiency of promotion delivery. Inthis regard, by providing a mechanism to carefully deliver impressionsonly to those consumers for which transmission enhances revenuegeneration potential, example embodiments increase the potency ofpromotions offered by the promotion and marketing service.

Incremental Booking Values and Consumer Modeling

Having described the circuitry comprising some example embodiments, itshould be understood that a promotion and marketing service 102 mayutilize statistical analysis to model historical consumer behavior andpredict expected incremental booking values of various promotions forvarious consumers. These concepts will be discussed in connection withFIGS. 5 through 10B.

As an initial matter, the inventors have identified that a determinationof the incremental booking value of a promotion is a particularly usefulmetric for evaluating the effectiveness of promotions and avoidingunnecessary waste caused by unneeded promotions that are offered toconsumers who would purchase a good, service, or experience without thepromotion. In this regard, the incremental booking value calculationrepresents an important insight that can be used in conjunction with aconsumer model to predict expected outcomes based on the historicalactions taken by consumers.

Calculating the incremental booking value associated with a promotionworks in the following manner. Assuming perfect knowledge, thecalculation comprises the difference between a first expected bookingvalue (e.g., the expected booking value that would be received from aconsumer having access to the promotion) and a second expected bookingvalue (e.g., one that would be received from the consumer without accessto the promotion). In general, a consumer is more likely to purchase aproduct if the consumer is provided with a promotion for the product, sothe first expected booking value is typically greater than the secondexpected booking value.

The incremental benefit of the promotion, then, constitutes thedifference between the two. However, because the promotion provides anincentive to the consumer (e.g., a discount), the actual incrementalbenefit of the promotion is the first expected booking value minus thesecond expected booking value minus the value of the incentive.

In some situations, the incremental booking value may be zero ornegative. This result is found in situations where the consumer has apreexisting interest in purchasing a product, thus the increase in theprobability of purchase from use of the promotion does not outweigh theloss from providing the incentive to the consumer.

The incremental booking value associated with a promotion for aparticular consumer essentially produces an indication of whether thepromotion is worth providing to the particular consumer. Thus, findingways to predict expected incremental booking value associated with apromotion for a consumer population enables two importantdeterminations. First, it provides an indication of whether there areany consumers for which the promotion will actually increase theexpected booking value to a promotion and marketing service. Second, itprovides a way to measure which consumers should be targeted with agiven promotion. However, utilizing the incremental booking valuecalculation requires knowledge of what a consumer will do with apromotion versus what the consumer will do without. This calculationcannot be accomplished without perfect information, though.

From this premise, the inventors identified a mechanism for training apredictive model that can evaluate consumer characteristics inconjunction with consumer behavior and historical information (e.g.,electronic marketing information), and can predict future behavior ofconsumers in particular scenarios based on consumer characteristics andhistorical behavior of similar or similarly situated consumers.

The information that may be utilized to train these models can beharvested by the promotion and marketing service 102. As discussedabove, this information may comprise electronic marketing information,which may encompass a wide array of different types of information(e.g., clickstream data, transaction data, location data, contextinformation, communication channel data, discretionary data, or anyother data stored by or received by the promotion and marketing servicefor use in providing electronic communications to consumers).

The inventors have identified several types of electronic marketinginformation, however, that provide notable predictive impact. Turningnow to FIG. 5, a series of these types of electronic marketinginformation are illustrated, along with the relative magnitudes of theirpredictive impact. In order of relative importance, these types ofelectronic marketing information include: “deal_purchase_cnt_030_d,”which indicates a number of purchases within a past thirty days;“num_purchases_d,” which indicates a number of purchases through thepromotion and marketing service; “mobile_email_click_cnt_030_d,” whichindicates a number of impressions received via the consumer's mobiledevice that have been clicked by the consumer; “total_gross_revenue_d,”which indicates a total revenue that the consumer has provided over allpromotions; “tenure_d,” which indicates tenure information (e.g., howlong a consumer has had an account with the promotion and marketingservice); “utm_medium_d,” which indicates the way that the consumer cameto the promotion and marketing service; “email_click_cnt_030_d,” whichindicates a number of impressions received via the consumer's regularemail that have been clicked on by the consumer; “offer_flag_d,” whichindicates whether the consumer has received an impression of aparticular promotion; “n_deals_w_3zip_030_d,” which indicates a numberof promotions purchased by the consumer within three zip codes of agiven location within the prior thirty days; “avg_booking_amount_d,”which indicates an average cost of each of the consumer's bookings;“active_channels_sub_cnt_d,” which indicates a number of active channelsubscriptions by the consumer; “total_booking_quantity_d,” whichindicates a total number of bookings that the consumer has made;“max_gross_revenue_d,” which indicates the highest price of anypromotion purchased by the consumer via the promotion and marketingservice; “n_pp_0_15_d,” which indicates a number of promotions between$0 and $15 that have been shown to the consumer; and “median_price_d,”which indicates a median price of the consumer's bookings.

It should be understood that any given characteristic of a firstconsumer likely only provides relatively weak predictive impact on theexpected actions of other consumers having that particularcharacteristic. Accordingly, example embodiments utilize machinelearning algorithms to combine the relatively weak impacts of a numberof consumer characteristics to strengthen the predictive capabilities ofthe model. In this regard, example embodiments contemplate the use ofensemble methods that are particularly well suited to combining manyweak “learners” in an attempt to produce a strong “learner.”

Within the class of ensemble statistical models, the inventors havediscovered that using gradient boosted regression produces aparticularly accurate model of future consumer behavior. A gradientboosted regression model (or any statistical model of this nature) canbe trained using a data set describing a control group of consumers whoare not provided with a promotion and a target group of consumers whoare provided with the promotion to estimate the expected impact of aseries of measured characteristics on a consumer's expected incrementalbooking value. For a given promotion, this model can then be used topredictively analyze the measured characteristics of a new consumer andpredict an expected incremental booking value associated with thatconsumer. Some example embodiments may then rank-order a consumerpopulation based on the expected incremental booking values of theconstitute consumers.

Turning now to FIGS. 6A and 6B, example incremental booking values areillustrated in graph form plotted against different deciles of arank-ordered consumer population. Historical data sets were used togenerate these graphs, and thus incremental booking values could becalculated using the actual purchase history of similar or similarlysituated consumers. FIG. 6A demonstrates a breakdown of per-consumerincremental booking values in different deciles and illustrates thatconsumers in the higher deciles demonstrate significantly higherexpected incremental booking values than those in lower deciles, whichsuggests that a top fraction of a consumer population may be veryeffectively targeted with a promotion, but that all portions of theconsumer population will produce added expected value. FIG. 6Bdemonstrates a breakdown of the total incremental booking values ingraph form plotted against total bookings for each decile of therank-ordered consumer population.

Turning now to the graphs illustrated in FIGS. 7A and 7B, the samedeciles of rank-ordered consumer population are shown graphed againstthe Contribution to Profit (“CP”) of the promotion. The CP illustratesthe profit to the promotion and marketing service after consideration ofthe margin of revenue that goes to the merchant. In contrast to theillustrations in FIGS. 6A and 6B, FIGS. 7A and 7B demonstrate that, evenif revenues are increased due to targeting lower-decile portions of theconsumer population with a promotion, the resulting profit to thepromotion and marketing service is negative for all groups save thehighest two deciles. In this regard, FIG. 7A illustrates the CPcontribution on a per-consumer basis, while FIG. 7B illustrates thetotal monetary effect of all consumers within each decile.

Turning now to FIGS. 8A and 8B, diagrams are provided that illustratethe monetary impact of offering promotions to different segments of theconsumer population. FIG. 8A illustrates the impact of offering apromotion (control 0) versus not offering a promotion (control 1) on thepurchase rate, the dollar value of total discounts provided, the dollarvalue of bookings per consumer, and the after-discount CP per consumerfor three categories: the entire consumer population, the top 30% of theconsumer population (ranked by incremental booking value) and the top20% of the consumer population (ranked by incremental booking value).

FIG. 8B, on the other hand, illustrates a summation of incrementalbooking values, incremental CP, and ROI for these different segments ofthe population. As shown in FIG. 8B, a promotion, when targeted at theentire population, may produce a negative ROI, but when targeted only atthe top 30% of the consumer population, demonstrates a slightly positiveROI, and when targeted at only the top 20% of the consumer population,demonstrates a significant increase in ROI by using example proceduresdescribed herein. Thus, this sample data set illustrates the value ofthe incremental booking value as a measure of effectiveness ofpromotional efforts.

FIGS. 9A and 9B illustrate the projected impact of offering promotionsto different segments of a consumer population, calculated using astatistical model of consumer behavior in accordance with an exampleembodiment. Similarly, FIGS. 10A and 10B illustrate the differentialimpact on incremental booking and CP, respectively, of offering apromotion to a top fraction of consumers in a population (arranged byprojected incremental booking values) versus offering the same promotionto a random fraction of the consumer population.

Example Operations

Having illustrated a correlation between the expected incrementalbooking values of a consumer population and the revenue derived fromthat consumer population, example operations performed by exampleembodiments will be described. It should be understood that thepromotion and marketing service 102 may utilize a consumer model asdescribed previously to predict the expected incremental booking valuesassociated with a population of consumer for various promotions. Usingthis predictive model, the promotion and marketing service 102 maytarget promotion impressions to a subset of the consumer population fora variety of reasons (e.g., to maximize the expected revenue derivedfrom a particular promotional offering). FIG. 11 illustrates a flowchartcontaining a broad description of such operations. FIG. 12 illustrates aflowchart providing greater description of some example operations forestimating incremental booking values associated with a promotion for aconsumer. FIG. 13 illustrates a flowchart providing greater descriptionof some example operations for selecting consumers for transmission ofimpressions. The operations illustrated in FIGS. 11, 12, and 13 may, forexample, be performed by a server 104 of a promotion and marketingservice 102, with the assistance of, and/or under the control of anapparatus 200.

In operation 1102, apparatus 200 includes means, such as processor 202,memory 204, input/output circuitry 206, communications circuitry 208, orthe like, for causing retrieval of information regarding a plurality ofconsumers. In some embodiments, this operation comprises causingretrieval of historical information regarding transactions associatedwith a plurality of identifier entities. In some embodiments, thisoperation includes receiving electronic marketing information from oneor more of the plurality of consumers or devices associated with theplurality of identifier entities, from one or more merchants, from amemory, or from a combination thereof. It should be understood that theelectronic marketing information may include at least one of: a numberof purchases within a past thirty days; a number of purchases made by aconsumer through a promotion and marketing service; a number ofimpressions received via a consumer's mobile device that have beenclicked by the consumer; a total revenue that a consumer has providedover all promotions; consumer tenure information; a path that a consumerused to sign up for a promotion and marketing service; a number ofimpressions received via a consumer's regular email that have beenclicked on by the consumer; an indication of whether a consumer hasreceived an impression of a particular promotion; a number of promotionspurchased by a consumer within three zip codes of a given locationwithin a prior thirty days; an average cost of each of a consumer'sbookings; a number of active channel subscriptions by a consumer; atotal number of bookings that a consumer has made; a highest price ofany promotion purchased by a consumer via a promotion and marketingservice; a number of promotions between $0 and $15 that have been shownto a consumer; and a median price of a consumer's bookings.

In some embodiments, the electronic marketing information may comprisehistorical information regarding transactions associated with aplurality of identifier entities, wherein the historical informationincludes, for each identifier entity, a number of purchases associatedwith the identifier entity within a predetermined period of time; anumber of purchases associated with the identifier entity through apromotion and marketing service; click indications regarding a number ofimpressions associated with the identifier entity; a total revenueassociated with the identifier entity over all promotions; registrationinformation associated with the identifier entity; email receiptindications regarding a number of email impressions associated with theidentifier entity; and impression receipt indications associated withthe identifier entity, a number of promotions purchased by theidentifier entity within three zip codes of a given location within apredetermined period of time; an average cost of transactions associatedwith the identifier entity; a number of active channel subscriptionsassociated with the identifier entity; a total number of bookingsassociated with the identifier entity; a highest price of a promotionpurchased from a promotion and marketing service that is associated withthe identifier entity; impression receipt indications regarding a numberof promotions in a predetermined price range associated with theidentifier entity; and a median price of bookings associated with theidentifier entity.

In operation 1104, apparatus 200 includes means, such as processor 202,modeling circuitry 210, or the like, for training a statistical model ofthe plurality of consumers or identifier entities based on the retrievedinformation. In some embodiments, the statistical model comprises anensemble learning model. In this regard, the ensemble learning model maycomprise a gradient boosted regression model. It should be understoodthat the ensemble learning model may alternative comprise a RandomForest, Support Vector Machine, or Stochastic Gradient Boosted DecisionsTree. Moreover, some example embodiments may utilize logisticregression, linear regression, neural networks, or rule based systemswith or without explicitly modeling feature interactions.

In operation 1106, apparatus 200 may optionally include means, such asprocessor 202, modeling circuitry 210, or the like, for predicting,using the statistical model, an expected incremental booking valueassociated with the promotion for each consumer of the plurality ofconsumers. In this regard, predicting expected incremental bookingvalues associated with a promotion is described in greater detail belowin association with FIG. 12.

In some embodiments, optional operation 1106 may alternatively includemeans, such as processor 202, modeling circuitry 210, or the like, forestimating using the statistical model, values for expected identifierentity transaction requests associated with each of the plurality ofidentifier entities. In this regard, an identifier entity transactionrequest may comprise a purchase transaction request that may or may notinclude a request to redeem a promotion. The values for an expectedidentifier entity transaction request may, in turn, comprise expectedtransaction data (e.g., expected purchase amounts, expected products orservices purchased, expected prices, expected promotion redemptionrequests, an expected incremental booking value, or the like).

In operation 1108, apparatus 200 includes means, such as processor 202,modeling circuitry 210, or the like, for selecting a subset of theplurality of consumers or identifier entities for receiving impressionsof the promotion. Selection of this subset is described in greaterdetail below in connection with FIG. 13.

Optionally, in operation 1110, apparatus 200 may also means, such ascommunications circuitry 208, or the like, for transmitting animpression of the promotion to each consumer (or identifier entity) inthe subset.

Turning now to FIG. 12, a description is provided of some exampleoperations for predicting an expected incremental booking valueassociated with a promotion for a consumer.

Turning first to operation 1202, the apparatus 200 includes means, suchas processor 202, modeling circuitry 210, or the like, for estimating,using the statistical model, a first expected booking value that wouldbe received from the consumer during a first time period in an instancein which the consumer has access to the promotion. In this regard, theconsumer model trained in operation 1104 may accept, as inputs,electronic marketing information regarding a particular consumer, andbased on the input electronic marketing information, the modelingcircuitry 210 may calculate an expected booking value based on pastbooking values associated with similar promotions and for similarlysituated consumers.

In operation 1204, apparatus 200 includes means, such as processor 202,modeling circuitry 210, or the like, for estimating, using thestatistical model, a second expected booking value that would bereceived from the consumer in an instance in which the consumer does nothave access to the promotion. The consumer modeling circuitry 210 maycalculate this expected booking value based on past booking valuesassociated for similarly situated consumers. It should be understoodthat operations 1202 and 1204 may be executed in any order and may evenbe performed in batch prior to the other operations of the proceduresdescribed herein.

In operation 1206, apparatus 200 includes means, such as processor 202,modeling circuitry 210, or the like, for calculating a first differencevalue by subtracting the second expected booking value from the firstexpected booking value. This calculation represents an important insightidentified by the inventors, which is that by enabling a comparison ofthe actions of similar, and similarly situated consumers, a consumermodel can predict expected outcomes based on the historical actionstaken by those similar and/or similarly situated consumers. In someembodiments, the expected incremental booking value may be set to thisfirst difference value, which represents the total revenue received as aresult of each transaction. It may not be necessary to factor in thediscount provided by application of the promotion if the only use of thecalculation will be to rank the consumers and the promotion comprises afixed discount campaigns (e.g., a $10 discount for all purchases). Forinstance, in such situations, any subsequent rankings based on the firstdifference would not be changed by factoring in this fixed discount. Inother embodiments, however, it is necessary to factor in the discount,in which case the operation proceeds to optional operation 1208.

Optionally, in operation 1208, apparatus 200 may include means, such asprocessor 202, modeling circuitry 210, or the like, for calculating asecond difference value by subtracting a discount value associated withthe promotion from the first difference value. In this regard, theexpected incremental booking value may be set to the second differencevalue, which represents the after-expense incremental gain in revenueproduced by offering a promotion to the consumer.

Finally, it should be noted that the operations of FIG. 12 are discussedin connection with estimation of an incremental booking value associatedwith a promotion for a single consumer. However, the operations in FIG.12 may in some embodiments be repeated for any number of consumers. Thisfact is illustrated in FIG. 12 by the dotted lines connecting operation1206 to operation 1202 (in an instance in which operation 1208 does notoccur) and operation 1208 to operation 1202 (in an instance in whichoperation 1208 takes place).

Turning now to FIG. 13, a description is provided of some exampleoperations selecting consumers to receive impressions of a promotion. Inoperation 1302, apparatus 200 includes means, such as processor 202,modeling circuitry 210, or the like, for ranking the plurality ofconsumers by incremental booking values associated with the promotionthat correspond to each of the plurality of consumers.

Subsequently, in operation 1304, apparatus 200 includes means, such asprocessor 202, modeling circuitry 210, or the like, for selecting apredetermined percentage of highest ranked consumers. It should beunderstood that the subset of the plurality of consumers to targetcomprises the predetermined percentage of highest ranked consumers. Thispredetermined percentage may be, for instance, a top 10%, 20%, or 30% ofranked consumers.

In some embodiments, selection of the subset of the plurality ofconsumers maximizes expected revenue generated by the promotion. To thisend, while the ranking of the consumers affects the expected revenuegenerated by the promotion, the size of the subset may also affect thisexpected revenue generation, and depending on preexisting interest inthe good, service, or experience offered by the promotion, increasing orshrinking the size of the subset may either increase or decrease theexpected revenue produced by offering the promotion. As a result,maximizing expected revenue may require utilizing a size that is not apredetermined percentage of highest ranked consumers, but instead is avariable size that is calculated to maximize the expected revenue fromoffering the promotion.

It should also be understood that maximizing expected revenue is not theonly metric that might be used to determine the set of users who receivethe promotion. In some embodiments, this set may be dictated by otherbusiness processes, such as the available budget for marketingpromotions, the merchant capacity, a predefined target incrementalrevenue desired from the marketing campaign, or the like. In any event,some embodiments may dynamically select the number of consumers toreceive a promotion, while other embodiments utilize a fixed number ofset of customers having certain characteristics.

FIGS. 11, 12, and 13 illustrate flowcharts of the operation of anapparatus, method, and computer program product according to exampleembodiments of the invention. It will be understood that each block ofthe flowcharts, and combinations of blocks in the flowcharts, may beimplemented by various means, such as hardware, firmware, processor,circuitry, and/or other devices associated with execution of softwareincluding one or more computer program instructions. For example, one ormore of the procedures described above may be embodied by computerprogram instructions. In this regard, the computer program instructionswhich embody the procedures described above may be stored by a memory ofan apparatus employing an embodiment of the present invention andexecuted by a processor of the apparatus. As will be appreciated, anysuch computer program instructions may be loaded onto a computer orother programmable apparatus (e.g., hardware) to produce a machine, suchthat the resulting computer or other programmable apparatus implementsthe functions specified in the flowchart blocks. These computer programinstructions may also be stored in a computer-readable memory that maydirect a computer or other programmable apparatus to function in aparticular manner, such that the instructions stored in thecomputer-readable memory produce an article of manufacture, theexecution of which implements the functions specified in the flowchartblocks. The computer program instructions may also be loaded onto acomputer or other programmable apparatus to cause a series of operationsto be performed on the computer or other programmable apparatus toproduce a computer-implemented process such that the instructionsexecuted on the computer or other programmable apparatus provideoperations for implementing the functions specified in the flowchartblocks.

Accordingly, blocks of the flowcharts support combinations of means forperforming the specified functions and combinations of operations forperforming the specified functions. It will be understood that one ormore blocks of the flowcharts, and combinations of blocks in theflowcharts, can be implemented by special purpose hardware-basedcomputer systems which preform the specified functions, or combinationsof special purpose hardware and computer instructions.

In some embodiments, certain ones of the operations above may bemodified or further amplified. Furthermore, in some embodiments,additional optional operations may be included. Modifications,amplifications, or additions to the operations above may be performed inany order and in any combination.

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Moreover, although the foregoing descriptions and the associateddrawings describe example embodiments in the context of certain examplecombinations of elements and/or functions, it should be appreciated thatdifferent combinations of elements and/or functions may be provided byalternative embodiments without departing from the scope of the appendedclaims. In this regard, for example, different combinations of elementsand/or functions than those explicitly described above are alsocontemplated as may be set forth in some of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

What is claimed is:
 1. An apparatus for improved machine learning usinga statistical model, the apparatus comprising: a processor configured tocause retrieval of information regarding a plurality of consumers;modeling circuitry configured to train the statistical model of theplurality of consumers based on the retrieved information regarding theplurality of consumers, and predict, using the statistical model, anincremental booking value associated with the promotion for eachconsumer of the plurality of consumers by: estimating, using thestatistical model, a first expected revenue, wherein the first expectedrevenue is estimated based on a first set of input informationcomprising at least a first promotion indicator indicating the consumerwould have access to the promotion for a first time period, the firstexpected revenue comprising first expected booking value, estimating,using the statistical model, a second expected revenue, wherein thesecond expected revenue is estimated based on a second set of inputinformation comprising at least a second promotion indicator indicatingthe consumer would not have access to the promotion, the second expectedrevenue comprising a second expected booking value, and calculating afirst difference value by subtracting the second expected revenue fromthe first expected revenue, wherein the predicted incremental bookingvalue comprises the first difference value; wherein the processor isfurther configured to select a subset of the plurality of consumers forwhom the predicted incremental booking value is above a predefinedthreshold; and communications circuitry configured to transmit animpression of the promotion to each consumer in the subset of theplurality of consumers.
 2. The apparatus of claim 1, wherein theprocessor is configured to cause retrieval of information regarding theplurality of consumers by causing the apparatus to: receive electronicmarketing information from one or more of the plurality of consumers,from one or more merchants, from a memory, or from a combinationthereof.
 3. The apparatus of claim 2, wherein the electronic marketinginformation includes at least one of: a number of purchases within apast thirty days; a number of purchases made by a consumer through apromotion and marketing service; a number of impressions received via aconsumer's mobile device that have been clicked by the consumer; a totalrevenue that a consumer has provided over all promotions; consumertenure information; a path that a consumer used to sign up for apromotion and marketing service; a number of impressions received via aconsumer's regular email that have been clicked on by the consumer; anindication of whether a consumer has received an impression of aparticular promotion; a number of promotions purchased by a consumerwithin three zip codes of a given location within a prior thirty days;an average cost of each of a consumer's bookings; a number of activechannel subscriptions by a consumer; a total number of bookings that aconsumer has made; a highest price of any promotion purchased by aconsumer via a promotion and marketing service; a number of promotionsbetween $0 and $15 that have been shown to a consumer; and a medianprice of a consumer's bookings.
 4. The apparatus of claim 1, wherein thestatistical model of the plurality of consumers comprises an ensemblelearning model.
 5. The apparatus of claim 4, wherein the ensemblelearning model comprises a gradient boosted regression model.
 6. Theapparatus of claim 1, wherein the processor is configured to: calculatea second difference value by subtracting a discount value associatedwith the promotion from the first difference value, wherein the expectedincremental booking value comprises the second difference value.
 7. Theapparatus of claim 1 wherein the processor is configured to select thesubset of the plurality of consumers for receiving impressions of thepromotion by: ranking the plurality of consumers by incremental bookingvalues associated with the promotion that correspond to each of theplurality of consumers; and selecting a predetermined percentage ofhighest ranked consumers, wherein the subset of the plurality ofconsumers to target comprises the predetermined percentage of highestranked consumers.
 8. The apparatus of claim 1, wherein selection of thesubset of the plurality of consumers maximizes expected revenuegenerated by the promotion.
 9. A method for improved machine learningusing a statistical model, the method comprising: retrieving informationregarding a plurality of consumers; training, by modeling circuitry, astatistical model of the plurality of consumers based on the retrievedinformation regarding the plurality of consumers; predicting, by themodeling circuitry and using the statistical model, an incrementalbooking value associated with the promotion for each consumer of theplurality of consumers by: estimating, using the statistical model, afirst expected revenue, wherein the first expected revenue is estimatedbased on a first set of input information comprising at least a firstpromotion indicator indicating the consumer would have access to thepromotion for a first time period, the first expected revenue comprisingfirst expected booking value, estimating, using the statistical model, asecond expected revenue, wherein the second expected revenue isestimated based on a second set of input information comprising at leasta second promotion indicator indicating the consumer would not haveaccess to the promotion, the second expected revenue comprising a secondexpected booking value, and calculating a first difference value bysubtracting the second expected revenue from the first expected revenue,wherein the predicted incremental booking value comprises the firstdifference value; selecting, by a processor, a subset of the pluralityof consumers for whom the predicted incremental booking value is above apredefined threshold; and transmitting, by communications circuitry, animpression of the promotion to each consumer in the subset of theplurality of consumers.
 10. The method of claim 9, wherein retrievinginformation regarding the plurality of consumers includes: receivingelectronic marketing information from one or more of the plurality ofconsumers, from one or more merchants, from a memory, or from acombination thereof.
 11. The method of claim 10, wherein the electronicmarketing information includes at least one of: a number of purchaseswithin a past thirty days; a number of purchases made by a consumerthrough a promotion and marketing service; a number of impressionsreceived via a consumer's mobile device that have been clicked by theconsumer; a total revenue that a consumer has provided over allpromotions; consumer tenure information; a path that a consumer used tosign up for a promotion and marketing service; a number of impressionsreceived via a consumer's regular email that have been clicked on by theconsumer; an indication of whether a consumer has received an impressionof a particular promotion; a number of promotions purchased by aconsumer within three zip codes of a given location within a priorthirty days; an average cost of each of a consumer's bookings; a numberof active channel subscriptions by a consumer; a total number ofbookings that a consumer has made; a highest price of any promotionpurchased by a consumer via a promotion and marketing service; a numberof promotions between $0 and $15 that have been shown to a consumer; anda median price of a consumer's bookings.
 12. The method of claim 9,wherein the statistical model of the plurality of consumers comprises anensemble learning model.
 13. The method of claim 12, wherein theensemble learning model comprises a gradient boosted regression model.14. The method of claim 9, further comprising: calculating a seconddifference value by subtracting a discount value associated with thepromotion from the first difference value, wherein the expectedincremental booking value comprises the second difference value.
 15. Themethod of claim 9 wherein selecting the subset of the plurality ofconsumers for receiving impressions of the promotion includes: rankingthe plurality of consumers by incremental booking values associated withthe promotion that correspond to each of the plurality of consumers; andselecting a predetermined percentage of highest ranked consumers,wherein the subset of the plurality of consumers to target comprises thepredetermined percentage of highest ranked consumers.
 16. The method ofclaim 9, wherein selecting the subset of the plurality of consumersmaximizes expected revenue generated by the promotion.
 17. A apparatusfor improved machine learning using a statistical model, the apparatuscomprising: means for retrieving information regarding a plurality ofconsumers; means for training a statistical model of the plurality ofconsumers based on the retrieved information regarding the plurality ofconsumers; means for predicting, using the statistical model, anincremental booking value associated with the promotion for eachconsumer of the plurality of consumers by: estimating, using thestatistical model, a first expected revenue, wherein the first expectedrevenue is estimated based on a first set of input informationcomprising at least a first promotion indicator indicating the consumerwould have access to the promotion for a first time period, the firstexpected revenue comprising first expected booking value, estimating,using the statistical model, a second expected revenue, wherein thesecond expected revenue is estimated based on a second set of inputinformation comprising at least a second promotion indicator indicatingthe consumer would not have access to the promotion, the second expectedrevenue comprising a second expected booking value, and calculating afirst difference value by subtracting the second expected revenue fromthe first expected revenue, wherein the predicted incremental bookingvalue comprises the first difference value; and means for selecting asubset of the plurality of consumers for whom the predicted incrementalbooking value is above a predefined threshold; and means fortransmitting an impression of the promotion to each consumer in thesubset of the plurality of consumers.
 18. The apparatus of claim 17,wherein the means for retrieving information regarding the plurality ofconsumers includes: means for receiving electronic marketing informationfrom one or more of the plurality of consumers, from one or moremerchants, from a memory, or from a combination thereof.
 19. Theapparatus of claim 18, wherein the electronic marketing informationincludes at least one of: a number of purchases within a past thirtydays; a number of purchases made by a consumer through a promotion andmarketing service; a number of impressions received via a consumer'smobile device that have been clicked by the consumer; a total revenuethat a consumer has provided over all promotions; consumer tenureinformation; a path that a consumer used to sign up for a promotion andmarketing service; a number of impressions received via a consumer'sregular email that have been clicked on by the consumer; an indicationof whether a consumer has received an impression of a particularpromotion; a number of promotions purchased by a consumer within threezip codes of a given location within a prior thirty days; an averagecost of each of a consumer's bookings; a number of active channelsubscriptions by a consumer; a total number of bookings that a consumerhas made; a highest price of any promotion purchased by a consumer via apromotion and marketing service; a number of promotions between $0 and$15 that have been shown to a consumer; and a median price of aconsumer's bookings.
 20. The apparatus of claim 17, wherein thestatistical model of the plurality of consumers comprises an ensemblelearning model.
 21. The apparatus of claim 20, wherein the ensemblelearning model comprises a gradient boosted regression model.
 22. Theapparatus of claim 17, further comprising: means for calculating asecond difference value by subtracting a discount value associated withthe promotion from the first difference value, wherein the expectedincremental booking value comprises the second difference value.
 23. Theapparatus of claim 17, wherein the means for selecting the subset of theplurality of consumers for receiving impressions of the promotionincludes: means for ranking the plurality of consumers by incrementalbooking values associated with the promotion that correspond to each ofthe plurality of consumers; and means for selecting a predeterminedpercentage of highest ranked consumers, wherein the subset of theplurality of consumers to target comprises the predetermined percentageof highest ranked consumers.
 24. The apparatus of claim 17, whereinselection of the subset of the plurality of consumers maximizes expectedrevenue generated by the promotion.